Jellyfish review, pricing and verdict

Last updated: 2026-11-06

Founded in 2017 in Boston, Jellyfish is an engineering intelligence platform that connects Git, issue tracker and AI coding tool data to delivery metrics, token spend and R&D finance reports. According to its homepage, the benchmarks draw on more than 100 million pull requests. The vendor lists SOC 2 Type II and SOC 1 Type II attestations.

About Jellyfish

Jellyfish is an engineering intelligence platform founded in 2017 and based in Boston. It reads data from source control, issue trackers, CI/CD and AI coding tools, then turns it into delivery metrics, AI usage and token cost figures, investment allocation and finance reports. Its homepage says the benchmarks behind the product draw on more than 1,000 engineering organizations, 100M+ pull requests, 90M+ prompts and 150T+ tokens, and it frames the product around software teams that now include AI agents as well as people.

The platform is sold as modules: AI Impact, Operational Effectiveness, Business Alignment, DevEx and DevFinOps. AI Impact covers adoption, token spend and productivity for coding tools, and Jellyfish's integrations page lists Claude Code from Anthropic, Cursor, GitHub Copilot, Codex and others, including review tools such as Qodo and Graphite. Code and pull request data comes from GitHub and other hosts, and tickets come from Jira, Linear or Azure Boards. Operational Effectiveness holds DORA metrics, cycle time, a Life Cycle Explorer and team benchmarks, and the Jellyfish Assistant answers questions about that data in plain language.

The buyer is usually a VP of engineering, a CTO or an engineering operations lead who has to show finance and the board what AI tooling returns. DevFinOps is the finance-facing module: software capitalization, R&D tax credits, investment allocations and audit-ready reports, with SOC 1 Type II compliance named on the pricing page. DevEx adds research-backed developer surveys beside the delivery data. Jellyfish runs as a web platform with no desktop or mobile app.

Pricing is not published. The pricing page lists three packages, AI Impact, Developer Productivity (which includes every AI Impact capability) and DevFinOps, and says each quote is set by seat count and module choice, so every buyer starts with a quote request. No free plan or trial is listed. By comparison, LinearB prints per-user prices, while Waydev also quotes through sales.

On security, the trust center names SOC 1 Type II and SOC 2 Type II attestations, GDPR, CCPA and CSA STAR, hosting entirely on AWS, TLS 1.2+ in transit and AES 256-bit encryption at rest. Jellyfish raised its Series A in 2020 and its B and C rounds in 2021 and 2022, putting total funding at $114.5M by its own account. It suits large engineering organizations that need delivery and finance reporting in one product, and it fits a ten-person team badly. If the open question is which assistants deserve a seat in the first place, read Hokai's guides to the best AI coding assistants and the best AI code review tools first.

Pricing

Jellyfish publishes no prices. Quoted by sales on seat count and module choice: the AI Impact, Developer Productivity and DevFinOps packages. No free plan or trial is listed.

Read from jellyfish.co/pricing on 2026-11-06. For comparison, LinearB prints per-user prices.

Key Features

• AI impact and token cost: Tracks adoption and usage of coding tools such as Claude Code, Cursor and GitHub Copilot, plus token spend by vendor, team and model, per the AI Impact pages.
• DORA and delivery metrics: Reports throughput, cycle time and DORA measures across people and agents, with Blueprints, custom metrics in plain English or SQL.
• Jellyfish Assistant: Answers questions about engineering data in plain language, finds root causes, and builds dashboards and monitors on request.
• DevEx surveys: Runs research-backed developer surveys beside delivery and AI data, to show where friction slows teams down.
• DevFinOps reporting: Produces software capitalization, R&D tax credit and investment allocation reports, with audit-ready output and SOC 1 Type II compliance.
• Cross-industry benchmarks: Compares teams against a benchmark set drawn from the vendor's own customer base.

Pros

• Five named modules cover AI coding tool adoption, token spend, DORA metrics, developer surveys and finance reporting under one login.
• The integrations page lists 12 AI coding tools, including Claude Code, Cursor, GitHub Copilot and Codex.
• Security attestations include the SOC 1 and SOC 2 Type II reports, GDPR, CCPA and CSA STAR, with AWS-only hosting and AES 256-bit encryption at rest.
• DevFinOps is sold as its own package for finance teams, so the engineering-only buyer does not have to pay for the reporting layer.
• The homepage cites a benchmark set of 1,000+ engineering organizations (a vendor figure).

Cons

• No price is published, so a team cannot compare cost with LinearB before a sales call.
• Evaluation starts with a demo request or a self-guided tour, since the pricing page offers neither a free plan nor a trial.
• Cost scales with seats and modules, and the pricing page does not say how the packages are metered.
• The vendor positions the product for enterprise engineering, so a team under about ten engineers will find it oversized.
• The G2 and Gartner scores of 4.5 out of 5 on the homepage are vendor-stated, and no review counts accompany them.

Visit Jellyfish Official Website